1. Field
The following description relates to image processing, and more particularly, relates to a method and apparatus for generating feature vectors for facial expression recognition.
2. Description of the Related Art
Facial expression is produced by activation of facial muscles which are triggered by nerve impulses. In image processing, facial expression recognition from visual cues is a technique of identifying a facial expression of an individual from a digital image or a video frame. A facial expression recognition system is generally employed to predict facial expression specific information based on a facial expression on a face of an individual. Typically, facial expression specific information is represented through a set of facial muscle action units (AUs). For example, when a new test image arrives, the facial expression recognition system first localizes facial muscle shapes, represents the localized facial muscle shapes using a feature descriptor, and classifies the representation against pre-stored AU models to obtain a facial expression on the face.
One of the challenges for accurate facial expression recognition is generating efficient and discriminative feature descriptors that are resistant to large variations of illumination, pose, face expression, aging, face misalignment, and other factors. One of the currently well used techniques comprises generating a feature descriptor by applying two-dimensional spatial filter banks that respond to edges and shapes at various orientations and scales. Another currently known technique generates a feature descriptor based on histogram of patterns derived from a relative change in parameters of local patches. In yet another currently known technique, relative distances of facial anchor points are coded as features. Some of the known techniques use a combination of the above techniques to generate feature descriptors. However, none of the currently known techniques are capable of generating efficient and discriminative feature descriptors.